Affiliation:
1. Beirut Arab University, Electrical and Computer Engineering Department,
Lebanon
Abstract
<div>Recent legislations require very low soot emissions downstream of the particulate
filter in diesel vehicles. It will be difficult to meet the new more stringent
OBD requirements with standard diagnostic methods based on differential sensors.
The use of inexpensive and reliable soot sensors has become the focus of several
academic and industrial works over the past decade. In this context, several
diagnostic strategies have been developed to detect DPF malfunction based on the
soot sensor loading time. This work proposes an advanced online diagnostic
method based on soot sensor signal projection. The proposed method is model-free
and exclusively uses soot sensor signal without the need for subsystem models or
to estimate engine-out soot emissions. It provides a comprehensive and efficient
filter monitoring scheme with light calibration efforts. The proposed diagnostic
algorithm has been tested on an experimentally validated simulation platform. 2D
signatures are generated from soot sensor signal for nominal and faulty
configurations. Gaussian dispersions on soot estimator (30%) and sensor model
(15%) have been considered. Based on a statistical analysis, a relevant
threshold is defined satisfying a compromise between non-detection and false
alarm rates. The selected threshold is then used for online DPF diagnostic using
NEDC cycle. The obtained results are promising and clearly show the performance
of the proposed method in terms of non-detection and false alarm rates. The
resulting diagnostic scheme can be easily integrated in the ECU for onboard DPF
monitoring.</div>
Subject
Fuel Technology,Automotive Engineering,General Earth and Planetary Sciences,General Environmental Science